Chenxia Jin, Fachao Li, Yuqing Xia, Sohail S. Chaudhry
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引用次数: 0
Abstract
AbstractThe existing shelf layout methods do not explicitly consider the attention and relevancy of the commodity systematically and thus have failed to capture the invalid associations, resulting in poor sales impact and customer satisfaction. For such shortcomings, in this paper, we propose a mathematical programming approach for shelf layout problems based on comprehensive related value. First, we introduce the concepts of related value considering both attention and relevancy; second, we give the concept of adjacent utility value and the freedom of placement, and further analyze the impact of the same commodity on surrounding commodities due to different placement positions; third, we establish a new comprehensive related value-based commodity layout optimization model (CRV-CL) and provide the solution steps integrating with a genetic algorithm. Finally, we analyze the characteristics of CRV-CL through a specific case. The simulation results indicate the overall relevancy after applying the CRV-CL model.Keywords: Shelf layoutcomprehensive related valuefreedom of placementadjacent utility valuegenetic algorithm Disclosure statementNo potential conflict of interest was reported by the author(s).Ethical approvalThis article does not contain any studies with human participants or animals performed by any of the authors.Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Grant (72101082); the Natural Science Foundation of Hebei Province under Grant (F2021208011). The research of Sohail S. Chaudhry was partially supported through a research sabbatical leave from Villanova University.
摘要现有的货架布置方法没有系统地明确考虑商品的关注度和相关性,未能捕捉到无效的关联,导致销售影响和顾客满意度较差。针对这些不足,本文提出了一种基于综合相关值的货架布置问题的数学规划方法。首先,我们引入了相关价值的概念,同时考虑了注意力和相关性;其次,给出相邻效用价值和放置自由度的概念,进一步分析同一商品由于放置位置不同对周边商品的影响;第三,建立了基于价值的综合相关商品布局优化模型(CRV-CL),并结合遗传算法给出了求解步骤。最后,通过具体案例分析CRV-CL的特点。仿真结果表明,采用CRV-CL模型后,总体上具有相关性。关键词:货架布置图综合相关价值放置自由相邻效用价值遗传算法披露声明作者未报告潜在利益冲突。伦理批准本文不包含任何作者进行的任何人类参与者或动物研究。项目资助:国家自然科学基金资助项目(72101082);河北省自然科学基金项目(F2021208011);Sohail S. Chaudhry的研究得到了Villanova大学的研究休假的部分支持。
期刊介绍:
The Journal of Management Analytics (JMA) is dedicated to advancing the theory and application of data analytics in traditional business fields. It focuses on the intersection of data analytics with key disciplines such as accounting, finance, management, marketing, production/operations management, and supply chain management. JMA is particularly interested in research that explores the interface between data analytics and these business areas. The journal welcomes studies employing a range of research methods, including empirical research, big data analytics, data science, operations research, management science, decision science, and simulation modeling.